The quick spread of coronavirus disease (COVID-19) has resulted in a global pandemic and more than fifteen million confirmed cases. To battle this spread, clinical imaging techniques, for example, computed tomography (CT), can be utilized for diagnosis. Automatic identification software tools are essential for helping to screen COVID-19 using CT images. However, there are few datasets available, making it difficult to train deep learning (DL) networks. To address this issue, a generative adversarial network (GAN) is proposed in this work to generate more CT images. The Whale Optimization Algorithm (WOA) is used to optimize the hyperparameters of GAN's generator. The proposed method is tested and validated with different classification and meta-heuristics algorithms using the SARS-CoV-2 CT-Scan dataset, consisting of COVID-19 and non-COVID-19 images. The performance metrics of the proposed optimized model, including accuracy (99.22%), sensitivity (99.78%), specificity (97.78%), F1-score (98.79%), positive predictive value (97.82%), and negative predictive value (99.77%), as well as its confusion matrix and receiver operating characteristic (ROC) curves, indicate that it performs better than state-of-the-art methods. This proposed model will help in the automatic screening of COVID-19 patients and decrease the burden on medicinal services frameworks.

冠状病毒病 (COVID-19) 的迅速传播已导致全球大流行和超过 1500 万例确诊病例。为了对抗这种传播，临床成像技术，例如计算机断层扫描 (CT)，可用于诊断。自动识别软件工具对于帮助使用 CT 图像筛查 COVID-19 至关重要。但是，可用的数据集很少，因此很难训练深度学习 (DL) 网络。为了解决这个问题，在这项工作中提出了一种生成对抗网络（GAN）来生成更多的 CT 图像。鲸鱼优化算法 (WOA) 用于优化 GAN 生成器的超参数。使用 SARS-CoV-2 CT-Scan 数据集通过不同的分类和元启发式算法对所提出的方法进行了测试和验证，由 COVID-19 和非 COVID-19 图像组成。提出的优化模型的性能指标，包括准确性（99.22%）、敏感性（99.78%）、特异性（97.78%）、F1-score（98.79%）、阳性预测值（97.82%）和阴性预测值（99.77） %），以及它的混淆矩阵和接收者操作特征（ROC）曲线，表明它比最先进的方法表现更好。这个提议的模型将有助于自动筛查 COVID-19 患者并减轻医疗服务框架的负担。以及它的混淆矩阵和接收者操作特征（ROC）曲线，表明它比最先进的方法表现更好。这个提议的模型将有助于自动筛查 COVID-19 患者并减轻医疗服务框架的负担。以及它的混淆矩阵和接收者操作特征（ROC）曲线，表明它比最先进的方法表现更好。这个提议的模型将有助于自动筛查 COVID-19 患者并减轻医疗服务框架的负担。